The concepts of validity and reliability are multi-faceted; there are many different types of validity and different types of reliability. Hence there will be several ways in which they can be addressed. It is unwise to think that threats to validity and reliability can ever be erased completely; rather, the effects of these threats can be attenuated by attention to validity and reliability throughout a piece of research. This chapter discusses validity and reliability in quantitative and qualitative, naturalistic research. It suggests that both of these terms can be applied to these two types of research, though how validity and reliability are addressed in these two approaches varies. Finally validity and reliability using different instruments for data collection are addressed. It is suggested that reliability is a necessary but insufficient condition for validity in research; reliability is a necessary precondition of validity. Brock-Utne (1996:612) contends that the widely held view that reliability is the sole preserve of quantitative research has to be exploded, and this chapter demonstrates the significance of her view.

Defining validity

Validity is an important key to effective research. If a piece of research is invalid then it is worthless. Validity is thus a requirement for both quantitative and qualitative/naturalistic research. Whilst earlier versions of validity were based on the view that it was essentially a demonstration that a particular instrument in fact measures what it purports to measure, more recently validity has taken many forms. For example, in qualitative data validity might be addressed through the honesty, depth, richness and scope of the data achieved, the participants approached, the extent of triangulation and the disinterestedness or objectivity of the researcher. In quantitative data validity might be improved through careful sampling, appropriate instrumentation and appropriate statistical treatments of the data. It is impossible for research to be 100 per cent valid; that is the optimism of perfection. Quantitative research possesses a measure of standard error which is inbuilt and which has to be acknowledged. In qualitative data the subjectivity of respondents, their opinions, attitudes and perspectives together contribute to a degree of bias. Validity, then, should be seen as a matter of degree rather than as an absolute state (Gronlund, 1981). Hence at best we strive to minimize invalidity and maximize validity. There are several different kinds of validity, for example:

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